OpenRaiser/Intern-Atlas huggingface dataset:
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
Venue: arXiv cs.AI 2022
Year: 2022

This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving simulation software, CARLA, which allows testing the approach's feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning.
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification
Venue: arXiv cs.LG 2022
Year: 2022

For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features and small number of available samples. Different types of omics data show various aspects of samples. Integration and analysis of multi-omics data give us a broad view of tumours, which can improve clinical decision making. Omics data, mainly DNA methylation and gene expression profiles are usually high dimensional data with a lot of molecular features. In recent years, variational autoencoders (VAE) have been extensively used in embedding image and text data into lower dimensional latent spaces. In our project, we extend the idea of using a VAE model for low dimensional latent space extraction with the self-supervised learning technique of feature subsetting. With VAEs, the key idea is to make the model learn meaningful representations from different types of omics data, which could then be used for downstream tasks such as cancer type classification. The main goals are to overcome the curse of dimensionality and integrate methylation and expression data to combine information about different aspects of same tissue samples, and hopefully extract biologically relevant features. Our extension involves training encoder and decoder to reconstruct the data from just a subset of it. By doing this, we force the model to encode most important information in the latent representation. We also added an identity to the subsets so that the model knows which subset is being fed into it during training and testing. We experimented with our approach and found that SubOmiEmbed produces comparable results to the baseline OmiEmbed with a much smaller network and by using just a subset of the data. This work can be improved to integrate mutation-based genomic data as well.
The relationship between sentiment score and COVID-19 cases in the United States
Venue: arXiv cs.CY 2022
Year: 2022

The coronavirus disease (COVID-19) continues to have devastating effects across the globe. No nation has been free from the uncertainty brought by this pandemic. The health, social and economic tolls associated with it are causing strong emotions and spreading fear in people of all ages, genders, and races. Since the beginning of the COVID-19 pandemic, many have expressed their feelings and opinions related to a wide range of aspects of their lives via Twitter. In this study, we consider a framework for extracting sentiment scores and opinions from COVID-19 related tweets. We connect users' sentiment with COVID-19 cases across the USA and investigate the effect of specific COVID-19 milestones on public sentiment. The results of this work may help with the development of pandemic-related legislation, serve as a guide for scientific work, as well as inform and educate the public on core issues related to the pandemic.
Developer Load Normalization Using Iterative Kuhn-Munkres Algorithm: An Optimization Triaging Approach
Venue: arXiv cs.SE 2022
Year: 2022

Bug triage can be defined as the process of assigning a developer to a bug report. The duty of the bug triage team is to study the developers profiles well in order to make an appropriate match between the developers and the incoming bug reports. Thus, this process is a vital step in issue management system. In fact, the number of bug reports submitted every day is gradually increasing which affects the developer workload. Thus, the triage team should consider this factor in distributing the bugs and because of the manual approach, many developers are burden. In particular, triaging bug reports without considering the workload does not only affect the developers workload but also leads to an increase in the number of unaddressed bug reports. As a result, the fixing time of the reported bugs will relatively increase. Unlike other researchers who focus on automating the bug triage and ignoring the developer workload, in this work, we handle the triaging process from a different perspective. The proposed approach focuses on how to optimize the bug fixing time by normalizing the developer load in an automating system. To evaluate our work, we use 26,317 bug reports from different bug repositories. Results shows that our work outperforms other systems in terms of optimizing the bug total fixing time and normalizing developer load.
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits
Venue: arXiv cs.LG 2022
Year: 2022

Methods for offline A/B testing and counterfactual learning are seeing rapid adoption in search and recommender systems, since they allow efficient reuse of existing log data. However, there are fundamental limits to using existing log data alone, since the counterfactual estimators that are commonly used in these methods can have large bias and large variance when the logging policy is very different from the target policy being evaluated. To overcome this limitation, we explore the question of how to design data-gathering policies that most effectively augment an existing dataset of bandit feedback with additional observations for both learning and evaluation. To this effect, this paper introduces Minimum Variance Augmentation Logging (MVAL), a method for constructing logging policies that minimize the variance of the downstream evaluation or learning problem. We explore multiple approaches to computing MVAL policies efficiently, and find that they can be substantially more effective in decreasing the variance of an estimator than naïve approaches.
RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds
Venue: arXiv cs.CG 2022
Year: 2022

The use of topological descriptors in modern machine learning applications, such as Persistence Diagrams (PDs) arising from Topological Data Analysis (TDA), has shown great potential in various domains. However, their practical use in applications is often hindered by two major limitations: the computational complexity required to compute such descriptors exactly, and their sensitivity to even low-level proportions of outliers. In this work, we propose to bypass these two burdens in a data-driven setting by entrusting the estimation of (vectorization of) PDs built on top of point clouds to a neural network architecture that we call RipsNet. Once trained on a given data set, RipsNet can estimate topological descriptors on test data very efficiently with generalization capacity. Furthermore, we prove that RipsNet is robust to input perturbations in terms of the 1-Wasserstein distance, a major improvement over the standard computation of PDs that only enjoys Hausdorff stability, yielding RipsNet to substantially outperform exactly-computed PDs in noisy settings. We showcase the use of RipsNet on both synthetic and real-world data. Our open-source implementation is publicly available at https://github.com/hensel-f/ripsnet and will be included in the Gudhi library.
PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators
Venue: arXiv cs.AR 2022
Year: 2022

In this work, PRUNIX, a framework for training and pruning convolutional neural networks is proposed for deployment on memristor crossbar based accelerators. PRUNIX takes into account the numerous non-ideal effects of memristor crossbars including weight quantization, state-drift, aging and stuck-at-faults. PRUNIX utilises a novel Group Sawtooth Regularization intended to improve non-ideality tolerance as well as sparsity, and a novel Adaptive Pruning Algorithm (APA) intended to minimise accuracy loss by considering the sensitivity of different layers of a CNN to pruning. We compare our regularization and pruning methods with other standards on multiple CNN architectures, and observe an improvement of 13% test accuracy when quantization and other non-ideal effects are accounted for with an overall sparsity of 85%, which is similar to other methods
On expressive rule-based logics
Venue: arXiv cs.LO 2022
Year: 2022

We investigate a family of rule-based logics. The focus is on very expressive languages. We provide a range of characterization results for the expressive powers of the logics and relate them with corresponding game systems.
Who will Leave a Pediatric Weight Management Program and When? -- A machine learning approach for predicting attrition patterns
Venue: arXiv cs.LG 2022
Year: 2022

Childhood obesity is a major public health concern. Multidisciplinary pediatric weight management programs are considered standard treatment for children with obesity and severe obesity who are not able to be successfully managed in the primary care setting; however, high drop-out rates (referred to as attrition) are a major hurdle in delivering successful interventions. Predicting attrition patterns can help providers reduce the attrition rates. Previous work has mainly focused on finding static predictors of attrition using statistical analysis methods. In this study, we present a machine learning model to predict (a) the likelihood of attrition, and (b) the change in body-mass index (BMI) percentile of children, at different time points after joining a weight management program. We use a five-year dataset containing the information related to around 4,550 children that we have compiled using data from the Nemours Pediatric Weight Management program. Our models show strong prediction performance as determined by high AUROC scores across different tasks (average AUROC of 0.75 for predicting attrition, and 0.73 for predicting weight outcomes). Additionally, we report the top features predicting attrition and weight outcomes in a series of explanatory experiments.
Improving Automatic Complexity Analysis of Integer Programs
Venue: arXiv cs.LO 2022
Year: 2022

In earlier work, we developed an approach for automatic complexity analysis of integer programs, based on an alternating modular inference of upper runtime and size bounds for program parts. In this paper, we show how recent techniques to improve automated termination analysis of integer programs (like the generation of multiphase-linear ranking functions and control-flow refinement) can be integrated into our approach for the inference of runtime bounds. The power of the resulting approach is demonstrated by an extensive experimental evaluation with our new re-implementation of the corresponding tool KoAT.
Understanding Digital Government Transformation
Venue: arXiv cs.CY 2022
Year: 2022

In today's era of innovation of technological progression, digitalisation has not only transformed individual lives but also has a prominent influence on business activities. The world is surviving in a global yet complex technological progression that not only changes the lives of civilians but is also transforming the public, private, and academic spheres of life. This research focuses on the digitalisation of governments, their challenges, and success factors. It is found that government faces difficulties in formulating strategies, proper planning, execution strategies, and a lack of organised information and expertise. However, success can be achieved by working on capabilities of the future workforce, creating leaders for tomorrow, generating digitalisation capabilities, and bringing a purpose-driven digitalisation before digital government transformation. Overall, the study's findings suggest that digital government transformation creates value, enhances relations, improves service delivery, grows economy, pushes economic activities, enhances citizen engagement, increases the policy implementation and their efficiency, and affects business growth positively.
Deep Surface Reconstruction from Point Clouds with Visibility Information
Venue: arXiv cs.CV 2022
Year: 2022

Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations. Sensor visibility, however, holds meaningful information regarding space occupancy and surface orientation. In this paper, we present two simple ways to augment raw point clouds with visibility information, so it can directly be leveraged by surface reconstruction networks with minimal adaptation. Our proposed modifications consistently improve the accuracy of generated surfaces as well as the generalization ability of the networks to unseen shape domains. Our code and data is available at https://github.com/raphaelsulzer/dsrv-data.
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking
Venue: arXiv cs.CV 2022
Year: 2022

Object detectors, which are widely deployed in security-critical systems such as autonomous vehicles, have been found vulnerable to patch hiding attacks. An attacker can use a single physically-realizable adversarial patch to make the object detector miss the detection of victim objects and undermine the functionality of object detection applications. In this paper, we propose ObjectSeeker for certifiably robust object detection against patch hiding attacks. The key insight in ObjectSeeker is patch-agnostic masking: we aim to mask out the entire adversarial patch without knowing the shape, size, and location of the patch. This masking operation neutralizes the adversarial effect and allows any vanilla object detector to safely detect objects on the masked images. Remarkably, we can evaluate ObjectSeeker's robustness in a certifiable manner: we develop a certification procedure to formally determine if ObjectSeeker can detect certain objects against any white-box adaptive attack within the threat model, achieving certifiable robustness. Our experiments demonstrate a significant (~10%-40% absolute and ~2-6x relative) improvement in certifiable robustness over the prior work, as well as high clean performance (~1% drop compared with undefended models).
HRBF-Fusion: Accurate 3D reconstruction from RGB-D data using on-the-fly implicits
Venue: arXiv cs.CV 2022
Year: 2022

Reconstruction of high-fidelity 3D objects or scenes is a fundamental research problem. Recent advances in RGB-D fusion have demonstrated the potential of producing 3D models from consumer-level RGB-D cameras. However, due to the discrete nature and limited resolution of their surface representations (e.g., point- or voxel-based), existing approaches suffer from the accumulation of errors in camera tracking and distortion in the reconstruction, which leads to an unsatisfactory 3D reconstruction. In this paper, we present a method using on-the-fly implicits of Hermite Radial Basis Functions (HRBFs) as a continuous surface representation for camera tracking in an existing RGB-D fusion framework. Furthermore, curvature estimation and confidence evaluation are coherently derived from the inherent surface properties of the on-the-fly HRBF implicits, which devote to a data fusion with better quality. We argue that our continuous but on-the-fly surface representation can effectively mitigate the impact of noise with its robustness and constrain the reconstruction with inherent surface smoothness when being compared with discrete representations. Experimental results on various real-world and synthetic datasets demonstrate that our HRBF-fusion outperforms the state-of-the-art approaches in terms of tracking robustness and reconstruction accuracy.
Practical Imitation Learning in the Real World via Task Consistency Loss
Venue: arXiv cs.RO 2022
Year: 2022

Recent work in visual end-to-end learning for robotics has shown the promise of imitation learning across a variety of tasks. Such approaches are expensive both because they require large amounts of real world training demonstrations and because identifying the best model to deploy in the real world requires time-consuming real-world evaluations. These challenges can be mitigated by simulation: by supplementing real world data with simulated demonstrations and using simulated evaluations to identify high performing policies. However, this introduces the well-known "reality gap" problem, where simulator inaccuracies decorrelate performance in simulation from that of reality. In this paper, we build on top of prior work in GAN-based domain adaptation and introduce the notion of a Task Consistency Loss (TCL), a self-supervised loss that encourages sim and real alignment both at the feature and action-prediction levels. We demonstrate the effectiveness of our approach by teaching a mobile manipulator to autonomously approach a door, turn the handle to open the door, and enter the room. The policy performs control from RGB and depth images and generalizes to doors not encountered in training data. We achieve 72% success across sixteen seen and unseen scenes using only ~16.2 hours of teleoperated demonstrations in sim and real. To the best of our knowledge, this is the first work to tackle latched door opening from a purely end-to-end learning approach, where the task of navigation and manipulation are jointly modeled by a single neural network.
On the Benefit of Cooperation in Relay Networks
Venue: arXiv cs.IT 2022
Year: 2022

This work addresses the cooperation facilitator (CF) model, in which network nodes coordinate through a rate limited communication device. For independent multiple-access channel (MAC) encoders, the CF model is known to show significant rate benefits, even when the rate of cooperation is negligible. Specifically, the benefit in MAC sum-rate, as a function of the cooperation rate $C_{CF}$, sometimes has an infinite slope at $C_{CF}=0$. This work studies the question of whether cooperation through a CF can yield similar infinite-slope benefits when applied to internal network encoders in which dependence among MAC transmitters can be established without the help of the CF. Towards this end, this work studies the CF model when applied to relay nodes of a single-source, single-terminal, diamond network consisting of a broadcast channel followed by a MAC. In the relay channel with orthogonal receiver components, careful generalization of the partial-decode-forward/compress-forward lower bound to the CF model yields sufficient conditions for an infinite-slope benefit. Additional results include derivation of a family of diamond networks for which the infinite-slope rate-benefit derives directly from the properties of the corresponding MAC component when studied in isolation.
Reduced polynomial invariant integrity basis for in-plane magneto-mechanical loading
Venue: arXiv cs.CE 2022
Year: 2022

The description of the behavior of a material subjected to multi-physics loadings requires the formulation of constitutive laws that usually derive from Gibbs free energies, using invariant quantities depending on the considered physics and material symmetries. On the other hand, most of crystalline materials can be described by their crystalline texture and the associated preferred directions of strong crystalline symmetry (the so-called fibers). Moreover, among the materials produced industrially, many are manufactured in the form of sheets or of thin layers. This article has for object the study of the magneto-mechanical coupling which is a function of the stress $σ$ and the magnetization M. We consider a material with cubic symmetry whose texture can be described by one of three fibers denoted as $θ$, $γ$ or $α$ ' , and which is thin enough so that both the stress and the magnetization can be considered as in-plane quantities. We propose an algorithm able to derive linear relations between the 30 cubic invariants I k of a minimal integrity basis describing a magneto-elastic problem, when they are restricted to in-plane loading conditions and for different fiber orientations. The algorithm/program output is a reduced list of invariants of cardinal 7 for the {100}-oriented $θ$ fiber, of cardinal 15 for the {110}-oriented $α$ ' fiber and of cardinal 8 for the {111}-oriented $γ$ fiber. This reduction (compared to initial cardinal 30) can be of great help for the formulation of low-parameter macroscopic magneto-mechanical models.
Active metric learning and classification using similarity queries
Venue: arXiv cs.LG 2022
Year: 2022

Active learning is commonly used to train label-efficient models by adaptively selecting the most informative queries. However, most active learning strategies are designed to either learn a representation of the data (e.g., embedding or metric learning) or perform well on a task (e.g., classification) on the data. However, many machine learning tasks involve a combination of both representation learning and a task-specific goal. Motivated by this, we propose a novel unified query framework that can be applied to any problem in which a key component is learning a representation of the data that reflects similarity. Our approach builds on similarity or nearest neighbor (NN) queries which seek to select samples that result in improved embeddings. The queries consist of a reference and a set of objects, with an oracle selecting the object most similar (i.e., nearest) to the reference. In order to reduce the number of solicited queries, they are chosen adaptively according to an information theoretic criterion. We demonstrate the effectiveness of the proposed strategy on two tasks -- active metric learning and active classification -- using a variety of synthetic and real world datasets. In particular, we demonstrate that actively selected NN queries outperform recently developed active triplet selection methods in a deep metric learning setting. Further, we show that in classification, actively selecting class labels can be reformulated as a process of selecting the most informative NN query, allowing direct application of our method.
Hybrid Neural Coded Modulation: Design and Training Methods
Venue: arXiv cs.IT 2022
Year: 2022

We propose a hybrid coded modulation scheme which composes of inner and outer codes. The outer-code can be any standard binary linear code with efficient soft decoding capability (e.g. low-density parity-check (LDPC) codes). The inner code is designed using a deep neural network (DNN) which takes the channel coded bits and outputs modulated symbols. For training the DNN, we propose to use a loss function that is inspired by the generalized mutual information. The resulting constellations are shown to outperform the conventional quadrature amplitude modulation (QAM) based coding scheme for modulation order 16 and 64 with 5G standard LDPC codes.